A goal of information visualization is to use computer graphics to utilize human perceptual processes in organizing and understanding data, such as may pertain to physical phenomena, including semantic domains. Informational elements that are to be visualized often have only semantic properties with no inherent spatial form that may be employed to create a visual representation. Accordingly, spatial ordering used in a display should be implemented as part of the visualization process. Viewed in this way, information visualization generally requires three components: data organization, visual spatial representations of the data organization, as well as display and interaction elements.
Various information visualization techniques have been implemented to display different types of data in an organized and logical manner. In general, information visualization maps data sets into visual media to facilitate analysis of the data and/or to communicate information about such data. Oftentimes, the data being represented has one or more unbounded dimensions, which has no natural beginning or end. A common example of an unbounded element is time, such as may be displayed in connection with a timeline (e.g., in a linear manner). A graphical visualization of a timeline, however, typically is cropped by the edges of the display.
In order to display a greater portion of time-based data, spacing between portions of the displayed representation may be adjusted, such that a region of interest is shown in greater detail than regions outside the region of interest. One example of such a technique is to construct the visualization according to the notion of a “fisheye” view of the data. In a fisheye visualization technique, side portions may be progressively more densely packed further away from the region of interest. As a result, a fisheye technique for visualizing data does not facilitate visualization of periodic data nor does it help view relationships between spaced apart items of data.
A two-dimensional spiral visualization attempts to overcome some of the shortcomings of the linear visualization techniques. When the period of a spiral is appropriately chosen, for example, a spiral visualization facilitates viewing periodic data, including periodic patterns associated with the data. Data that occurs in time following a pattern is easily viewable with a spiral representation. Spirals also have been utilized to arrange a list of items into a compact space and for showing correspondence between different granularities of information. Typically, a spiral representation of linear information is represented as a flat spiral, in which different values of the data are associated with different segments along the spiral. An advantage of the spiral visualization of linear data is its compactness and scalability. However, such a visualization tends to create false impressions concerning the duration or level of importance of different data. For example, segments of data near the periphery of a spiral usually have a greater arc length than the segments near the center even though they do not actually have a longer duration in time.